In [265]:
import numpy
import matplotlib.pyplot as plt
import pandas
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
#データ読み込み Yは最初の列に配置する
dataframe = pandas.read_csv('c:/dev/dl/tokyo-weather-2003-2012.csv', usecols=[0,3,4,5,6], engine='python', skipfooter=1)
plt.plot(dataframe)
plt.show()
print(dataframe.head())
ice_sales avg_temp total_rain humidity num_day_over25deg
0 331 9.3 101.0 46 0
1 268 9.9 53.5 52 0
2 365 12.7 159.5 49 0
3 492 19.2 121.0 61 3
4 632 22.4 172.5 65 7
In [266]:
dataset = dataframe.values
dataset = dataset.astype('float32')
# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
# split into train and test sets
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
print(len(train), len(test))
79 40
In [267]:
# convert an array of values into a dataset matrix
# if you give look_back 3, a part of the array will be like this: Jan, Feb, Mar
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
xset = []
for j in range(dataset.shape[1]):
a = dataset[i:(i+look_back), j]
xset.append(a)
dataY.append(dataset[i + look_back, 0])
dataX.append(xset)
return numpy.array(dataX), numpy.array(dataY)
# reshape into X=t and Y=t+1
look_back = 12
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
print(testX.shape)
print(testX[0])
print(testY)
# reshape input to be [samples, time steps(number of variables), features] *convert time series into column
trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], trainX.shape[2]))
testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1], testX.shape[2]))
(27, 5, 12)
[[ 0.80050719 0.38461539 0.19188505 0.11327136 0.14539307 0.07945901
0.03127643 0.0972105 0.16568047 0.40912935 0.4843618 0.79712594]
[ 0.86507934 0.72222215 0.55555546 0.34523803 0.16269836 0.10714284
0.06349203 0.19444442 0.32936507 0.58333331 0.76190466 0.92460316]
[ 0.30714747 0.06374758 0.35157761 0.19059885 0.10173857 0.00708307
0.14359304 0.18029621 0.27108824 0.14230523 0.13457824 0.0856407 ]
[ 0.84615391 0.71794873 0.71794873 0.69230765 0.38461536 0.12820512
0.61538464 0.6410256 0.66666669 0.61538464 0.79487187 0.87179488]
[ 0.96774191 0.6774193 0.16129032 0.03225806 0. 0. 0.
0. 0.03225806 0.25806451 0.77419353 1. ]]
[ 1. 0.50380385 0.19949281 0.07016063 0.13102284 0.06593406
0.01775149 0.05156383 0.16398987 0.34150466 0.44209638 0.8427726
0.82248521 0.42180899 0.20963651 0.10566357 0.13102284 0.0600169
0.00507186 0.09890109 0.21639898 0.34065935 0.44801351 0.75824177
0.89940822 0.49112424 0.20879123]
In [268]:
# create and fit the LSTM network
model = Sequential()
model.add(LSTM(4, input_shape=(testX.shape[1], look_back))) #shape：変数数、遡る時間数
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=1000, batch_size=1, verbose=2)
Epoch 1/1000
1s - loss: 0.1030
Epoch 2/1000
0s - loss: 0.0602
Epoch 3/1000
0s - loss: 0.0411
Epoch 4/1000
0s - loss: 0.0287
Epoch 5/1000
0s - loss: 0.0218
Epoch 6/1000
0s - loss: 0.0175
Epoch 7/1000
0s - loss: 0.0147
Epoch 8/1000
0s - loss: 0.0127
Epoch 9/1000
0s - loss: 0.0115
Epoch 10/1000
0s - loss: 0.0099
Epoch 11/1000
0s - loss: 0.0088
Epoch 12/1000
0s - loss: 0.0079
Epoch 13/1000
0s - loss: 0.0071
Epoch 14/1000
0s - loss: 0.0063
Epoch 15/1000
0s - loss: 0.0056
Epoch 16/1000
0s - loss: 0.0053
Epoch 17/1000
0s - loss: 0.0050
Epoch 18/1000
0s - loss: 0.0047
Epoch 19/1000
0s - loss: 0.0044
Epoch 20/1000
0s - loss: 0.0043
Epoch 21/1000
0s - loss: 0.0040
Epoch 22/1000
0s - loss: 0.0040
Epoch 23/1000
0s - loss: 0.0039
Epoch 24/1000
0s - loss: 0.0037
Epoch 25/1000
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Epoch 26/1000
0s - loss: 0.0038
Epoch 27/1000
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Epoch 28/1000
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Epoch 29/1000
0s - loss: 0.0036
Epoch 30/1000
0s - loss: 0.0033
Epoch 31/1000
0s - loss: 0.0035
Epoch 32/1000
0s - loss: 0.0033
Epoch 33/1000
0s - loss: 0.0032
Epoch 34/1000
0s - loss: 0.0031
Epoch 35/1000
0s - loss: 0.0032
Epoch 36/1000
0s - loss: 0.0030
Epoch 37/1000
0s - loss: 0.0030
Epoch 38/1000
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Epoch 39/1000
0s - loss: 0.0029
Epoch 40/1000
0s - loss: 0.0028
Epoch 41/1000
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Epoch 42/1000
0s - loss: 0.0029
Epoch 43/1000
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Epoch 44/1000
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Epoch 45/1000
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Epoch 46/1000
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Epoch 47/1000
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Epoch 48/1000
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Epoch 49/1000
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Epoch 50/1000
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Epoch 51/1000
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Epoch 52/1000
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Epoch 53/1000
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Epoch 54/1000
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Epoch 55/1000
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Epoch 56/1000
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Epoch 57/1000
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Epoch 58/1000
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Epoch 59/1000
0s - loss: 0.0021
Epoch 60/1000
0s - loss: 0.0021
Epoch 61/1000
0s - loss: 0.0021
Epoch 62/1000
0s - loss: 0.0020
Epoch 63/1000
0s - loss: 0.0020
Epoch 64/1000
0s - loss: 0.0021
Epoch 65/1000
0s - loss: 0.0020
Epoch 66/1000
0s - loss: 0.0020
Epoch 67/1000
0s - loss: 0.0018
Epoch 68/1000
0s - loss: 0.0023
Epoch 69/1000
0s - loss: 0.0019
Epoch 70/1000
0s - loss: 0.0019
Epoch 71/1000
0s - loss: 0.0019
Epoch 72/1000
0s - loss: 0.0018
Epoch 73/1000
0s - loss: 0.0018
Epoch 74/1000
0s - loss: 0.0021
Epoch 75/1000
0s - loss: 0.0020
Epoch 76/1000
0s - loss: 0.0018
Epoch 77/1000
0s - loss: 0.0017
Epoch 78/1000
0s - loss: 0.0017
Epoch 79/1000
0s - loss: 0.0018
Epoch 80/1000
0s - loss: 0.0016
Epoch 81/1000
0s - loss: 0.0017
Epoch 82/1000
0s - loss: 0.0016
Epoch 83/1000
0s - loss: 0.0017
Epoch 84/1000
0s - loss: 0.0017
Epoch 85/1000
0s - loss: 0.0017
Epoch 86/1000
0s - loss: 0.0016
Epoch 87/1000
0s - loss: 0.0017
Epoch 88/1000
0s - loss: 0.0017
Epoch 89/1000
0s - loss: 0.0017
Epoch 90/1000
0s - loss: 0.0016
Epoch 91/1000
0s - loss: 0.0015
Epoch 92/1000
0s - loss: 0.0017
Epoch 93/1000
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Epoch 94/1000
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Epoch 95/1000
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Epoch 96/1000
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Epoch 97/1000
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Epoch 98/1000
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Epoch 99/1000
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Epoch 100/1000
0s - loss: 0.0015
Epoch 101/1000
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Epoch 102/1000
0s - loss: 0.0016
Epoch 103/1000
0s - loss: 0.0015
Epoch 104/1000
0s - loss: 0.0015
Epoch 105/1000
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Epoch 106/1000
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Epoch 107/1000
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Epoch 108/1000
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Epoch 109/1000
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Epoch 110/1000
0s - loss: 0.0014
Epoch 111/1000
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Epoch 112/1000
0s - loss: 0.0015
Epoch 113/1000
0s - loss: 0.0016
Epoch 114/1000
0s - loss: 0.0015
Epoch 115/1000
0s - loss: 0.0014
Epoch 116/1000
0s - loss: 0.0014
Epoch 117/1000
0s - loss: 0.0014
Epoch 118/1000
0s - loss: 0.0016
Epoch 119/1000
0s - loss: 0.0013
Epoch 120/1000
0s - loss: 0.0016
Epoch 121/1000
0s - loss: 0.0014
Epoch 122/1000
0s - loss: 0.0013
Epoch 123/1000
0s - loss: 0.0018
Epoch 124/1000
0s - loss: 0.0013
Epoch 125/1000
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Epoch 126/1000
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Epoch 127/1000
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Epoch 128/1000
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Epoch 129/1000
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Epoch 130/1000
0s - loss: 0.0014
Epoch 131/1000
0s - loss: 0.0012
Epoch 132/1000
0s - loss: 0.0013
Epoch 133/1000
0s - loss: 0.0012
Epoch 134/1000
0s - loss: 0.0011
Epoch 135/1000
0s - loss: 0.0013
Epoch 136/1000
0s - loss: 0.0014
Epoch 137/1000
0s - loss: 0.0012
Epoch 138/1000
0s - loss: 0.0012
Epoch 139/1000
0s - loss: 0.0013
Epoch 140/1000
0s - loss: 0.0013
Epoch 141/1000
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Epoch 142/1000
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Epoch 143/1000
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Epoch 144/1000
0s - loss: 0.0013
Epoch 145/1000
0s - loss: 0.0013
Epoch 146/1000
0s - loss: 0.0012
Epoch 147/1000
0s - loss: 0.0013
Epoch 148/1000
0s - loss: 0.0013
Epoch 149/1000
0s - loss: 0.0012
Epoch 150/1000
0s - loss: 0.0013
Epoch 151/1000
0s - loss: 0.0012
Epoch 152/1000
0s - loss: 0.0012
Epoch 153/1000
0s - loss: 0.0013
Epoch 154/1000
0s - loss: 0.0013
Epoch 155/1000
0s - loss: 0.0012
Epoch 156/1000
0s - loss: 0.0013
Epoch 157/1000
0s - loss: 0.0011
Epoch 158/1000
0s - loss: 0.0011
Epoch 159/1000
0s - loss: 0.0012
Epoch 160/1000
0s - loss: 0.0012
Epoch 161/1000
0s - loss: 0.0011
Epoch 162/1000
0s - loss: 0.0011
Epoch 163/1000
0s - loss: 0.0011
Epoch 164/1000
0s - loss: 0.0011
Epoch 165/1000
0s - loss: 0.0012
Epoch 166/1000
0s - loss: 0.0012
Epoch 167/1000
0s - loss: 0.0015
Epoch 168/1000
0s - loss: 0.0011
Epoch 169/1000
0s - loss: 0.0012
Epoch 170/1000
0s - loss: 0.0011
Epoch 171/1000
0s - loss: 0.0011
Epoch 172/1000
0s - loss: 0.0011
Epoch 173/1000
0s - loss: 0.0011
Epoch 174/1000
0s - loss: 0.0011
Epoch 175/1000
0s - loss: 0.0011
Epoch 176/1000
0s - loss: 0.0011
Epoch 177/1000
0s - loss: 0.0010
Epoch 178/1000
0s - loss: 9.9124e-04
Epoch 179/1000
0s - loss: 0.0012
Epoch 180/1000
0s - loss: 0.0011
Epoch 181/1000
0s - loss: 0.0010
Epoch 182/1000
0s - loss: 0.0011
Epoch 183/1000
0s - loss: 9.6737e-04
Epoch 184/1000
0s - loss: 0.0010
Epoch 185/1000
0s - loss: 0.0011
Epoch 186/1000
0s - loss: 0.0010
Epoch 187/1000
0s - loss: 0.0010
Epoch 188/1000
0s - loss: 0.0011
Epoch 189/1000
0s - loss: 9.6337e-04
Epoch 190/1000
0s - loss: 9.9464e-04
Epoch 191/1000
0s - loss: 0.0010
Epoch 192/1000
0s - loss: 0.0011
Epoch 193/1000
0s - loss: 9.6689e-04
Epoch 194/1000
0s - loss: 0.0011
Epoch 195/1000
0s - loss: 0.0012
Epoch 196/1000
0s - loss: 8.8451e-04
Epoch 197/1000
0s - loss: 0.0010
Epoch 198/1000
0s - loss: 9.4490e-04
Epoch 199/1000
0s - loss: 9.2825e-04
Epoch 200/1000
0s - loss: 9.8176e-04
Epoch 201/1000
0s - loss: 9.4779e-04
Epoch 202/1000
0s - loss: 9.6169e-04
Epoch 203/1000
0s - loss: 9.8370e-04
Epoch 204/1000
0s - loss: 0.0011
Epoch 205/1000
0s - loss: 8.7018e-04
Epoch 206/1000
0s - loss: 0.0010
Epoch 207/1000
0s - loss: 0.0010
Epoch 208/1000
0s - loss: 8.4432e-04
Epoch 209/1000
0s - loss: 8.9933e-04
Epoch 210/1000
0s - loss: 9.4567e-04
Epoch 211/1000
0s - loss: 8.3587e-04
Epoch 212/1000
0s - loss: 0.0010
Epoch 213/1000
0s - loss: 8.9661e-04
Epoch 214/1000
0s - loss: 9.0427e-04
Epoch 215/1000
0s - loss: 0.0010
Epoch 216/1000
0s - loss: 8.2905e-04
Epoch 217/1000
0s - loss: 8.7202e-04
Epoch 218/1000
0s - loss: 9.4573e-04
Epoch 219/1000
0s - loss: 9.1666e-04
Epoch 220/1000
0s - loss: 8.6440e-04
Epoch 221/1000
0s - loss: 0.0011
Epoch 222/1000
0s - loss: 8.2672e-04
Epoch 223/1000
0s - loss: 9.7442e-04
Epoch 224/1000
0s - loss: 9.0094e-04
Epoch 225/1000
0s - loss: 8.6098e-04
Epoch 226/1000
0s - loss: 7.5789e-04
Epoch 227/1000
0s - loss: 7.8330e-04
Epoch 228/1000
0s - loss: 7.9614e-04
Epoch 229/1000
0s - loss: 7.9444e-04
Epoch 230/1000
0s - loss: 8.4770e-04
Epoch 231/1000
0s - loss: 7.9805e-04
Epoch 232/1000
0s - loss: 8.7161e-04
Epoch 233/1000
0s - loss: 8.0195e-04
Epoch 234/1000
0s - loss: 7.6217e-04
Epoch 235/1000
0s - loss: 7.4264e-04
Epoch 236/1000
0s - loss: 7.8316e-04
Epoch 237/1000
0s - loss: 8.3823e-04
Epoch 238/1000
0s - loss: 7.0456e-04
Epoch 239/1000
0s - loss: 8.0806e-04
Epoch 240/1000
0s - loss: 8.1792e-04
Epoch 241/1000
0s - loss: 7.1973e-04
Epoch 242/1000
0s - loss: 8.3181e-04
Epoch 243/1000
0s - loss: 8.4316e-04
Epoch 244/1000
0s - loss: 7.5948e-04
Epoch 245/1000
0s - loss: 6.3389e-04
Epoch 246/1000
0s - loss: 7.6455e-04
Epoch 247/1000
0s - loss: 7.3370e-04
Epoch 248/1000
0s - loss: 8.2606e-04
Epoch 249/1000
0s - loss: 7.2659e-04
Epoch 250/1000
0s - loss: 8.2795e-04
Epoch 251/1000
0s - loss: 7.1806e-04
Epoch 252/1000
0s - loss: 8.3639e-04
Epoch 253/1000
0s - loss: 7.8744e-04
Epoch 254/1000
0s - loss: 7.0486e-04
Epoch 255/1000
0s - loss: 6.7023e-04
Epoch 256/1000
0s - loss: 6.9119e-04
Epoch 257/1000
0s - loss: 7.4455e-04
Epoch 258/1000
0s - loss: 5.5707e-04
Epoch 259/1000
0s - loss: 8.1531e-04
Epoch 260/1000
0s - loss: 6.3985e-04
Epoch 261/1000
0s - loss: 6.9077e-04
Epoch 262/1000
0s - loss: 7.2632e-04
Epoch 263/1000
0s - loss: 6.8306e-04
Epoch 264/1000
0s - loss: 6.5214e-04
Epoch 265/1000
0s - loss: 6.6835e-04
Epoch 266/1000
0s - loss: 7.5036e-04
Epoch 267/1000
0s - loss: 6.2332e-04
Epoch 268/1000
0s - loss: 6.6652e-04
Epoch 269/1000
0s - loss: 6.4486e-04
Epoch 270/1000
0s - loss: 6.7380e-04
Epoch 271/1000
0s - loss: 6.2057e-04
Epoch 272/1000
0s - loss: 5.9350e-04
Epoch 273/1000
0s - loss: 7.1243e-04
Epoch 274/1000
0s - loss: 5.7569e-04
Epoch 275/1000
0s - loss: 6.1908e-04
Epoch 276/1000
0s - loss: 5.7576e-04
Epoch 277/1000
0s - loss: 5.5497e-04
Epoch 278/1000
0s - loss: 6.0937e-04
Epoch 279/1000
0s - loss: 5.9175e-04
Epoch 280/1000
0s - loss: 5.9491e-04
Epoch 281/1000
0s - loss: 6.5507e-04
Epoch 282/1000
0s - loss: 5.8325e-04
Epoch 283/1000
0s - loss: 5.7169e-04
Epoch 284/1000
0s - loss: 4.9561e-04
Epoch 285/1000
0s - loss: 5.8557e-04
Epoch 286/1000
0s - loss: 5.2364e-04
Epoch 287/1000
0s - loss: 6.1091e-04
Epoch 288/1000
0s - loss: 6.3729e-04
Epoch 289/1000
0s - loss: 5.6879e-04
Epoch 290/1000
0s - loss: 5.7839e-04
Epoch 291/1000
0s - loss: 5.7017e-04
Epoch 292/1000
0s - loss: 5.6581e-04
Epoch 293/1000
0s - loss: 5.1533e-04
Epoch 294/1000
0s - loss: 5.1534e-04
Epoch 295/1000
0s - loss: 5.9127e-04
Epoch 296/1000
0s - loss: 5.1048e-04
Epoch 297/1000
0s - loss: 5.0006e-04
Epoch 298/1000
0s - loss: 5.4916e-04
Epoch 299/1000
0s - loss: 5.0792e-04
Epoch 300/1000
0s - loss: 5.4304e-04
Epoch 301/1000
0s - loss: 6.5800e-04
Epoch 302/1000
0s - loss: 5.2058e-04
Epoch 303/1000
0s - loss: 5.3689e-04
Epoch 304/1000
0s - loss: 5.1546e-04
Epoch 305/1000
0s - loss: 5.2494e-04
Epoch 306/1000
0s - loss: 5.3325e-04
Epoch 307/1000
0s - loss: 5.3704e-04
Epoch 308/1000
0s - loss: 4.6058e-04
Epoch 309/1000
0s - loss: 5.0729e-04
Epoch 310/1000
0s - loss: 5.0731e-04
Epoch 311/1000
0s - loss: 4.5361e-04
Epoch 312/1000
0s - loss: 4.5842e-04
Epoch 313/1000
0s - loss: 5.6533e-04
Epoch 314/1000
0s - loss: 4.9233e-04
Epoch 315/1000
0s - loss: 5.3421e-04
Epoch 316/1000
0s - loss: 4.6993e-04
Epoch 317/1000
0s - loss: 5.3152e-04
Epoch 318/1000
0s - loss: 5.3158e-04
Epoch 319/1000
0s - loss: 4.5845e-04
Epoch 320/1000
0s - loss: 5.0043e-04
Epoch 321/1000
0s - loss: 4.7045e-04
Epoch 322/1000
0s - loss: 4.3993e-04
Epoch 323/1000
0s - loss: 5.0282e-04
Epoch 324/1000
0s - loss: 4.6136e-04
Epoch 325/1000
0s - loss: 5.3652e-04
Epoch 326/1000
0s - loss: 4.7798e-04
Epoch 327/1000
0s - loss: 5.2758e-04
Epoch 328/1000
0s - loss: 5.0658e-04
Epoch 329/1000
0s - loss: 5.1017e-04
Epoch 330/1000
0s - loss: 4.9804e-04
Epoch 331/1000
0s - loss: 4.5066e-04
Epoch 332/1000
0s - loss: 4.8116e-04
Epoch 333/1000
0s - loss: 4.0092e-04
Epoch 334/1000
0s - loss: 4.4232e-04
Epoch 335/1000
0s - loss: 5.4357e-04
Epoch 336/1000
0s - loss: 4.3502e-04
Epoch 337/1000
0s - loss: 4.5639e-04
Epoch 338/1000
0s - loss: 3.7747e-04
Epoch 339/1000
0s - loss: 4.7917e-04
Epoch 340/1000
0s - loss: 4.4103e-04
Epoch 341/1000
0s - loss: 4.3262e-04
Epoch 342/1000
0s - loss: 4.2494e-04
Epoch 343/1000
0s - loss: 5.3555e-04
Epoch 344/1000
0s - loss: 4.2337e-04
Epoch 345/1000
0s - loss: 3.7400e-04
Epoch 346/1000
0s - loss: 4.4110e-04
Epoch 347/1000
0s - loss: 4.8967e-04
Epoch 348/1000
0s - loss: 4.7095e-04
Epoch 349/1000
0s - loss: 4.1895e-04
Epoch 350/1000
0s - loss: 4.1409e-04
Epoch 351/1000
0s - loss: 4.7774e-04
Epoch 352/1000
0s - loss: 5.7803e-04
Epoch 353/1000
0s - loss: 4.7738e-04
Epoch 354/1000
0s - loss: 4.1296e-04
Epoch 355/1000
0s - loss: 4.3332e-04
Epoch 356/1000
0s - loss: 4.0819e-04
Epoch 357/1000
0s - loss: 3.7840e-04
Epoch 358/1000
0s - loss: 5.3022e-04
Epoch 359/1000
0s - loss: 4.0621e-04
Epoch 360/1000
0s - loss: 4.3978e-04
Epoch 361/1000
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Epoch 362/1000
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Epoch 363/1000
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Epoch 364/1000
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Epoch 365/1000
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Epoch 366/1000
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Epoch 367/1000
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Epoch 368/1000
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Epoch 369/1000
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Epoch 370/1000
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Epoch 371/1000
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Epoch 372/1000
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Epoch 373/1000
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Epoch 374/1000
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Epoch 375/1000
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Epoch 376/1000
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Epoch 377/1000
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Epoch 378/1000
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Epoch 379/1000
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Epoch 380/1000
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Epoch 381/1000
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Epoch 382/1000
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Epoch 383/1000
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Epoch 384/1000
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Epoch 385/1000
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Epoch 386/1000
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Epoch 387/1000
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Epoch 388/1000
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Epoch 389/1000
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Epoch 390/1000
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Epoch 391/1000
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Epoch 392/1000
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Epoch 393/1000
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Epoch 394/1000
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Epoch 395/1000
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Epoch 396/1000
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Epoch 397/1000
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Epoch 398/1000
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Epoch 399/1000
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Epoch 400/1000
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Epoch 401/1000
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Epoch 402/1000
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Epoch 403/1000
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Epoch 404/1000
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Epoch 405/1000
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Epoch 406/1000
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Epoch 407/1000
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Epoch 408/1000
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Epoch 409/1000
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Epoch 410/1000
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Epoch 411/1000
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Epoch 412/1000
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Epoch 413/1000
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Epoch 414/1000
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Epoch 415/1000
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Epoch 416/1000
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Epoch 417/1000
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Epoch 418/1000
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Epoch 419/1000
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Epoch 420/1000
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Epoch 421/1000
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Epoch 422/1000
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Epoch 423/1000
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Epoch 424/1000
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Epoch 425/1000
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Epoch 426/1000
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Epoch 427/1000
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Epoch 428/1000
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Epoch 429/1000
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Epoch 430/1000
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Epoch 431/1000
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Epoch 432/1000
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Epoch 433/1000
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Epoch 434/1000
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Epoch 435/1000
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Epoch 436/1000
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Epoch 437/1000
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Epoch 438/1000
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Epoch 439/1000
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Epoch 440/1000
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Epoch 441/1000
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Epoch 442/1000
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Epoch 443/1000
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Epoch 444/1000
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Epoch 445/1000
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Epoch 446/1000
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Epoch 447/1000
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Epoch 448/1000
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Epoch 449/1000
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Epoch 450/1000
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Epoch 451/1000
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Epoch 452/1000
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Epoch 453/1000
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Epoch 454/1000
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Epoch 455/1000
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Epoch 456/1000
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Epoch 457/1000
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Epoch 458/1000
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Epoch 459/1000
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Epoch 460/1000
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Epoch 461/1000
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Epoch 462/1000
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Epoch 463/1000
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Epoch 464/1000
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Epoch 465/1000
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Epoch 466/1000
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Epoch 467/1000
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Epoch 468/1000
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Epoch 469/1000
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Epoch 470/1000
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Epoch 471/1000
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Epoch 472/1000
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Epoch 473/1000
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Epoch 474/1000
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Epoch 475/1000
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Epoch 476/1000
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Epoch 477/1000
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Epoch 478/1000
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Epoch 479/1000
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Epoch 480/1000
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Epoch 481/1000
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Epoch 482/1000
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Epoch 483/1000
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Epoch 484/1000
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Epoch 485/1000
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Epoch 486/1000
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Epoch 487/1000
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Epoch 488/1000
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Epoch 489/1000
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Epoch 490/1000
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Epoch 491/1000
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Epoch 492/1000
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Epoch 493/1000
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Epoch 494/1000
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Epoch 495/1000
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Epoch 496/1000
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Epoch 497/1000
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Epoch 498/1000
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Epoch 499/1000
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Epoch 500/1000
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Epoch 501/1000
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Epoch 502/1000
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Epoch 503/1000
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Epoch 504/1000
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Epoch 505/1000
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Epoch 506/1000
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Epoch 507/1000
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Epoch 508/1000
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Epoch 509/1000
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Epoch 510/1000
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Epoch 511/1000
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Epoch 512/1000
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Epoch 513/1000
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Epoch 514/1000
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Epoch 515/1000
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Epoch 516/1000
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Epoch 517/1000
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Epoch 518/1000
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Epoch 519/1000
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Epoch 520/1000
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Epoch 521/1000
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Epoch 522/1000
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Epoch 523/1000
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Epoch 524/1000
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Epoch 525/1000
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Epoch 526/1000
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Epoch 527/1000
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Epoch 528/1000
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Epoch 529/1000
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Epoch 530/1000
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Epoch 531/1000
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Epoch 532/1000
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Epoch 533/1000
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Epoch 534/1000
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Epoch 535/1000
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Epoch 536/1000
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Epoch 537/1000
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Epoch 538/1000
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Epoch 539/1000
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Epoch 540/1000
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Epoch 541/1000
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Epoch 542/1000
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Epoch 543/1000
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Epoch 544/1000
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Epoch 545/1000
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Epoch 546/1000
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Epoch 547/1000
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Epoch 548/1000
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Epoch 549/1000
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Epoch 550/1000
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Epoch 551/1000
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Epoch 552/1000
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Epoch 553/1000
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Epoch 554/1000
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Epoch 555/1000
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Epoch 556/1000
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Epoch 557/1000
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Epoch 558/1000
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Epoch 559/1000
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Epoch 560/1000
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Epoch 561/1000
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Epoch 562/1000
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Epoch 563/1000
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Epoch 564/1000
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Epoch 565/1000
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Epoch 566/1000
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Epoch 567/1000
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Epoch 568/1000
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Epoch 569/1000
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Epoch 570/1000
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Epoch 571/1000
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Epoch 572/1000
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Epoch 573/1000
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Epoch 574/1000
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Epoch 575/1000
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Epoch 576/1000
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Epoch 577/1000
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Epoch 578/1000
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Epoch 579/1000
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Epoch 580/1000
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Epoch 581/1000
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Epoch 582/1000
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Epoch 583/1000
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Epoch 584/1000
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Epoch 585/1000
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Epoch 586/1000
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Epoch 587/1000
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Epoch 588/1000
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Epoch 589/1000
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Epoch 590/1000
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Epoch 591/1000
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Epoch 592/1000
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Epoch 593/1000
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Epoch 594/1000
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Epoch 595/1000
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Epoch 596/1000
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Epoch 597/1000
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Epoch 598/1000
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Epoch 599/1000
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Epoch 600/1000
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Epoch 601/1000
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Epoch 602/1000
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Epoch 603/1000
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Epoch 604/1000
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Epoch 605/1000
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Epoch 606/1000
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Epoch 607/1000
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Epoch 608/1000
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Epoch 609/1000
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Epoch 610/1000
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Epoch 611/1000
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Epoch 612/1000
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Epoch 613/1000
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Epoch 614/1000
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Epoch 615/1000
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Epoch 616/1000
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Epoch 617/1000
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Epoch 618/1000
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Epoch 619/1000
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Epoch 620/1000
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Epoch 621/1000
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Epoch 622/1000
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Epoch 623/1000
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Epoch 624/1000
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Epoch 625/1000
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Epoch 626/1000
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Epoch 627/1000
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Epoch 628/1000
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Epoch 629/1000
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Epoch 630/1000
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Epoch 631/1000
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Epoch 632/1000
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Epoch 633/1000
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Epoch 634/1000
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Epoch 635/1000
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Epoch 636/1000
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Epoch 637/1000
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Epoch 638/1000
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Epoch 639/1000
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Epoch 640/1000
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Epoch 641/1000
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Epoch 642/1000
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Epoch 643/1000
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Epoch 644/1000
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Epoch 645/1000
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Epoch 646/1000
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Epoch 647/1000
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Epoch 648/1000
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Epoch 649/1000
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Epoch 650/1000
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Epoch 651/1000
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Epoch 652/1000
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Epoch 653/1000
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Epoch 654/1000
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Epoch 655/1000
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Epoch 656/1000
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Epoch 657/1000
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Epoch 658/1000
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Epoch 659/1000
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Epoch 660/1000
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Epoch 661/1000
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Epoch 662/1000
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Epoch 663/1000
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Epoch 664/1000
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Epoch 665/1000
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Epoch 666/1000
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Epoch 667/1000
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Epoch 668/1000
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Epoch 669/1000
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Epoch 670/1000
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Epoch 671/1000
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Epoch 672/1000
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Epoch 673/1000
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Epoch 674/1000
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Epoch 675/1000
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Epoch 676/1000
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Epoch 677/1000
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Epoch 678/1000
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Epoch 679/1000
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Epoch 680/1000
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Epoch 681/1000
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Epoch 682/1000
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Epoch 683/1000
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Epoch 684/1000
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Epoch 685/1000
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Epoch 686/1000
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Epoch 687/1000
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Epoch 688/1000
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Epoch 689/1000
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Epoch 690/1000
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Epoch 691/1000
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Epoch 692/1000
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Epoch 693/1000
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Epoch 694/1000
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Epoch 695/1000
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Epoch 696/1000
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Epoch 697/1000
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Epoch 698/1000
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Epoch 699/1000
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Epoch 700/1000
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Epoch 701/1000
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Epoch 702/1000
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Epoch 703/1000
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Epoch 704/1000
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Epoch 705/1000
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Epoch 706/1000
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Epoch 707/1000
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Epoch 708/1000
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Epoch 709/1000
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Epoch 710/1000
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Epoch 711/1000
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Epoch 712/1000
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Epoch 713/1000
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Epoch 714/1000
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Epoch 715/1000
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Epoch 716/1000
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Epoch 717/1000
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Epoch 718/1000
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Epoch 719/1000
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Epoch 720/1000
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Epoch 721/1000
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Epoch 722/1000
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Epoch 723/1000
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Epoch 724/1000
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Epoch 725/1000
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Epoch 726/1000
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Epoch 727/1000
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Epoch 728/1000
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Epoch 729/1000
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Epoch 730/1000
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Epoch 731/1000
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Epoch 732/1000
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Epoch 733/1000
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Epoch 734/1000
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Epoch 735/1000
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Epoch 736/1000
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Epoch 737/1000
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Epoch 738/1000
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Epoch 739/1000
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Epoch 740/1000
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Epoch 741/1000
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Epoch 742/1000
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Epoch 743/1000
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Epoch 744/1000
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Epoch 745/1000
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Epoch 746/1000
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Epoch 747/1000
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Epoch 748/1000
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Epoch 749/1000
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Epoch 750/1000
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Epoch 751/1000
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Epoch 752/1000
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Epoch 753/1000
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Epoch 754/1000
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Epoch 755/1000
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Epoch 756/1000
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Epoch 757/1000
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Epoch 758/1000
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Epoch 759/1000
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Epoch 760/1000
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Epoch 761/1000
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Epoch 762/1000
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Epoch 763/1000
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Epoch 764/1000
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Epoch 765/1000
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Epoch 766/1000
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Epoch 767/1000
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Epoch 768/1000
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Epoch 769/1000
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Epoch 770/1000
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Epoch 771/1000
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Epoch 772/1000
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Epoch 773/1000
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Epoch 774/1000
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Epoch 775/1000
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Epoch 776/1000
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Epoch 777/1000
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Epoch 778/1000
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Epoch 779/1000
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Epoch 780/1000
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Epoch 781/1000
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Epoch 782/1000
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Epoch 783/1000
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Epoch 784/1000
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Epoch 785/1000
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Epoch 786/1000
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Epoch 787/1000
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Epoch 788/1000
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Epoch 789/1000
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Epoch 790/1000
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Epoch 791/1000
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Epoch 792/1000
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Epoch 793/1000
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Epoch 794/1000
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Epoch 795/1000
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Epoch 796/1000
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Epoch 797/1000
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Epoch 798/1000
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Epoch 799/1000
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Epoch 800/1000
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Epoch 801/1000
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Epoch 802/1000
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Epoch 803/1000
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Epoch 804/1000
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Epoch 805/1000
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Epoch 806/1000
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Epoch 807/1000
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Epoch 808/1000
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Epoch 809/1000
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Epoch 810/1000
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Epoch 811/1000
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Epoch 812/1000
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Epoch 813/1000
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Epoch 814/1000
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Epoch 815/1000
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Epoch 816/1000
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Epoch 817/1000
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Epoch 818/1000
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Epoch 819/1000
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Epoch 820/1000
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Epoch 821/1000
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Epoch 822/1000
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Epoch 823/1000
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Epoch 824/1000
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Epoch 825/1000
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Epoch 826/1000
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Epoch 827/1000
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Epoch 828/1000
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Epoch 829/1000
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Epoch 830/1000
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Epoch 831/1000
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Epoch 832/1000
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Epoch 833/1000
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Epoch 834/1000
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Epoch 835/1000
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Epoch 836/1000
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Epoch 837/1000
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Epoch 838/1000
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Epoch 839/1000
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Epoch 840/1000
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Epoch 841/1000
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Epoch 842/1000
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Epoch 843/1000
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Epoch 844/1000
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Epoch 845/1000
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Epoch 846/1000
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Epoch 847/1000
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Epoch 848/1000
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Epoch 849/1000
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Epoch 850/1000
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Epoch 851/1000
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Epoch 852/1000
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Epoch 853/1000
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Epoch 854/1000
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Epoch 855/1000
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Epoch 856/1000
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Epoch 857/1000
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Epoch 858/1000
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Epoch 859/1000
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Epoch 860/1000
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Epoch 861/1000
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Epoch 862/1000
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Epoch 863/1000
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Epoch 864/1000
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Epoch 865/1000
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Epoch 866/1000
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Epoch 867/1000
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Epoch 868/1000
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Epoch 869/1000
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Epoch 870/1000
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Epoch 871/1000
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Epoch 872/1000
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Epoch 873/1000
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Epoch 874/1000
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Epoch 875/1000
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Epoch 876/1000
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Epoch 877/1000
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Epoch 878/1000
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Epoch 879/1000
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Epoch 880/1000
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Epoch 881/1000
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Epoch 882/1000
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Epoch 883/1000
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Epoch 884/1000
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Epoch 885/1000
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Epoch 886/1000
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Epoch 887/1000
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Epoch 888/1000
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Epoch 889/1000
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Epoch 890/1000
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Epoch 891/1000
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Epoch 892/1000
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Epoch 893/1000
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Epoch 894/1000
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Epoch 895/1000
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Epoch 896/1000
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Epoch 897/1000
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Epoch 898/1000
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Epoch 899/1000
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Epoch 900/1000
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Epoch 901/1000
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Epoch 902/1000
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Epoch 903/1000
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Epoch 904/1000
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Epoch 905/1000
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Epoch 906/1000
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Epoch 907/1000
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Epoch 908/1000
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Epoch 909/1000
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Epoch 910/1000
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Epoch 911/1000
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Epoch 912/1000
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Epoch 913/1000
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Epoch 914/1000
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Epoch 915/1000
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Epoch 916/1000
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Epoch 917/1000
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Epoch 918/1000
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Epoch 919/1000
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Epoch 920/1000
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Epoch 921/1000
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Epoch 922/1000
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Epoch 923/1000
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Epoch 924/1000
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Epoch 925/1000
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Epoch 926/1000
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Epoch 927/1000
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Epoch 928/1000
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Epoch 929/1000
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Epoch 930/1000
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Epoch 931/1000
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Epoch 932/1000
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Epoch 933/1000
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Epoch 934/1000
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Epoch 935/1000
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Epoch 936/1000
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Epoch 937/1000
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Epoch 938/1000
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Epoch 939/1000
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Epoch 940/1000
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Epoch 941/1000
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Epoch 942/1000
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Epoch 943/1000
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Epoch 944/1000
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Epoch 945/1000
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Epoch 946/1000
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Epoch 947/1000
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Epoch 948/1000
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Epoch 949/1000
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Epoch 950/1000
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Epoch 951/1000
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Epoch 952/1000
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Epoch 953/1000
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Epoch 954/1000
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Epoch 955/1000
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Epoch 956/1000
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Epoch 957/1000
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Epoch 958/1000
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Epoch 959/1000
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Epoch 960/1000
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Epoch 961/1000
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Epoch 962/1000
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Epoch 963/1000
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Epoch 964/1000
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Epoch 965/1000
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Epoch 966/1000
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Epoch 967/1000
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Epoch 968/1000
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Epoch 969/1000
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Epoch 970/1000
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Epoch 971/1000
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Epoch 972/1000
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Epoch 973/1000
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Epoch 974/1000
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Epoch 975/1000
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Epoch 976/1000
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Epoch 977/1000
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Epoch 978/1000
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Epoch 979/1000
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Epoch 980/1000
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Epoch 981/1000
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Epoch 982/1000
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Epoch 983/1000
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Epoch 984/1000
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Epoch 985/1000
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Epoch 986/1000
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Epoch 987/1000
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Epoch 988/1000
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Epoch 989/1000
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Epoch 990/1000
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Epoch 991/1000
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Epoch 992/1000
0s - loss: 3.1983e-05
Epoch 993/1000
0s - loss: 2.7399e-05
Epoch 994/1000
0s - loss: 3.9767e-05
Epoch 995/1000
0s - loss: 2.5913e-05
Epoch 996/1000
0s - loss: 4.3855e-05
Epoch 997/1000
0s - loss: 5.4059e-05
Epoch 998/1000
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Epoch 999/1000
0s - loss: 2.6321e-04
Epoch 1000/1000
0s - loss: 1.7997e-04
Out[268]:
<keras.callbacks.History at 0x2303c048>
In [264]:
# make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
pad_col = numpy.zeros(dataset.shape[1]-1)
# invert predictions
def pad_array(val):
return numpy.array([numpy.insert(pad_col, 0, x) for x in val])
trainPredict = scaler.inverse_transform(pad_array(trainPredict))
trainY = scaler.inverse_transform(pad_array(trainY))
testPredict = scaler.inverse_transform(pad_array(testPredict))
testY = scaler.inverse_transform(pad_array(testY))
# calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(trainY[:,0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[:,0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
Train Score: 30.20 RMSE
Test Score: 111.97 RMSE
In [258]:
print(testY[:,0])
print(testPredict[:,0])
# shift train predictions for plotting
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
# shift test predictions for plotting
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
# plot baseline and predictions
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()
[ 1451.00000179 863.99995152 503.99999229 351.00001666 423.00001556
345.99998981 289.00000096 329.00000427 462.0000135 672.00001322
791.00001777 1264.99998995 1241.00000207 767.00002989 515.99998623
392.99999546 423.00001556 338.99998747 274.00000854 384.99998774
523.99999394 671.00000785 797.99998485 1165.00001694 1331.99992673
848.99997673 515.00001611]
[ 1059.85835788 793.28683727 522.24769289 342.00613815 416.33646264
340.4159625 291.66957883 385.71532616 516.43599901 657.70160051
732.47766847 984.90223677 1179.21903281 696.6758262 522.03947008
376.28810553 401.53958088 330.33752556 282.08873835 363.97376777
487.15337099 628.04462271 750.29034347 964.59638804 1300.55532123
740.47771144 528.47565611]
In [ ]:
Content source: tizuo/keras
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